An empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis
Gabriele B Schweikert ⋅ Christian Widmer ⋅ Bernhard Schölkopf ⋅ Gunnar Rätsch
2008 Poster
Abstract
We study the problem of domain transfer for a supervised classification task in mRNA splicing. We consider a number of recent domain transfer methods from machine learning, including some that are novel, and evaluate them on genomic sequence data from model organisms of varying evolutionary distance. We find that in cases where the organisms are not closely related, the use of domain adaptation methods can help improve classification performance.
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